AI governance that enables,
not blocks, innovation.
Build a governance framework that keeps your organization compliant, trustworthy, and moving fast. Policy templates, risk classification, and implementation support from regulated-industry veterans.
An AI governance framework establishes accountability, transparency, data governance, risk assessment, monitoring, and incident response for enterprise AI systems. It enables responsible AI adoption while managing regulatory, reputational, and operational risk across the organization.
Source: What About AI? Business Services
AI is moving faster than policy
Your teams adopted AI tools months ago. Your governance is still catching up. That gap is where risk lives.
Without governance, you are exposed to:
Reputational Risk
Biased AI outputs, data breaches, or controversial automation decisions become front-page news. Rebuilding trust costs 10x what governance costs.
Legal Liability
Regulatory fines, class-action lawsuits, and enforcement actions are accelerating. The EU AI Act alone carries penalties up to 7% of global revenue.
Employee Distrust
When teams do not understand how AI decisions are made -or who is accountable- adoption stalls, shadow AI proliferates, and top talent questions your leadership.
Inconsistent AI Quality
Without standards, every team implements AI differently. No shared evaluation criteria, no monitoring, no way to know which AI systems are performing and which are silently failing.
Why we understand this: James spent years at one of the world's largest financial institutions, where AI governance is not optional — it is a regulatory requirement. Sean has led Fortune 500 transformation programs where governance frameworks were the difference between successful AI adoption and costly rollbacks. We build governance that works in practice, not just in policy documents.
The Governance Framework
Six pillars that cover every dimension of responsible AI. We customize each to your industry, scale, and regulatory environment.
Accountability
Who owns AI decisions in your organization? Without clear ownership, AI initiatives stall in committee or run unchecked. A RACI matrix for AI ensures every model, dataset, and deployment has a named owner.
Key questions to answer:
- Who approves new AI use cases?
- Who is responsible when an AI system produces a harmful output?
- Who reviews model performance on a recurring basis?
- How are AI decisions escalated to human reviewers?
Implementation guidance:
Start by mapping your current AI initiatives to existing business owners. AI governance should extend your existing accountability structures, not create a parallel bureaucracy.
Transparency
Explainability is no longer optional. Customers, regulators, and employees all want to understand how AI-driven decisions are made. Transparency builds trust and reduces legal exposure.
Key questions to answer:
- Can you explain how each AI system reaches its decisions?
- Are model inputs and outputs documented?
- Do affected individuals know when AI is involved in decisions about them?
- Is there a public-facing AI use disclosure?
Implementation guidance:
Document every AI system with a model card: purpose, training data summary, known limitations, and contact person. This becomes your explainability baseline.
Data Governance
Your AI is only as good as the data it trains on. Bias in, bias out. Data governance ensures training data is representative, privacy-compliant, and traceable to its source.
Key questions to answer:
- Where does your training data come from? Is it properly licensed?
- How do you detect and mitigate bias in datasets?
- Are you compliant with data privacy regulations (GDPR, CCPA)?
- Can you trace any AI output back to its training data?
Implementation guidance:
Implement a data lineage system before scaling AI adoption. Retroactively documenting data sources becomes exponentially harder as you add more models.
Risk Assessment
Not all AI uses carry equal risk. A chatbot answering product FAQs is fundamentally different from an AI making loan decisions. Tiered risk classification lets you apply proportional governance.
Key questions to answer:
- Have you categorized AI use cases by impact level (low, medium, high)?
- What criteria determine the risk tier of a new AI initiative?
- Are high-risk applications subject to additional review before deployment?
- How often are risk classifications revisited?
Implementation guidance:
Use a three-tier model: Low (internal productivity tools), Medium (customer-facing features with human oversight), High (autonomous decisions affecting individuals). Each tier gets proportional review requirements.
Monitoring & Audit
AI systems drift. Models that performed well at launch degrade as the world changes around them. Continuous monitoring catches performance decay, bias emergence, and unexpected behaviors before they become crises.
Key questions to answer:
- Do you monitor model performance metrics in production?
- How do you detect data drift or concept drift?
- Is there a schedule for periodic model audits?
- Who reviews audit findings, and what triggers remediation?
Implementation guidance:
Set up automated alerting on key performance metrics from day one. Quarterly human audits catch what automated monitoring misses: context shifts, ethical concerns, and changing stakeholder expectations.
Incident Response
When AI makes a mistake -and it will- your response speed and quality define your organization. An AI incident response plan is as critical as your cybersecurity incident plan.
Key questions to answer:
- What constitutes an AI incident in your organization?
- Who is notified, and how quickly, when an incident occurs?
- Can you roll back or disable a problematic AI system within hours?
- How are incidents documented and used to improve governance?
Implementation guidance:
Create a dedicated AI incident playbook separate from your general IT incident process. AI failures have unique characteristics: they can be subtle, systemic, and affect protected classes disproportionately.
The Compliance Landscape
The regulatory environment is evolving rapidly. Here is what you need to be aware of -and planning for.
EU AI Act
Phased enforcement 2024-2027All AI systems deployed in or affecting EU citizens
- Tiered risk classification (unacceptable, high, limited, minimal)
- Mandatory conformity assessments for high-risk AI
- Transparency obligations for all AI systems
- Prohibited practices: social scoring, real-time biometric surveillance
NIST AI Risk Management Framework
Published January 2023, updates ongoingUS federal guidance (voluntary, but increasingly referenced)
- Govern, Map, Measure, Manage lifecycle approach
- Emphasis on socio-technical context
- Risk identification across AI system lifecycle
- Stakeholder engagement requirements
Industry-Specific Regulations
Active enforcement, evolving interpretationSector-dependent (healthcare, finance, employment)
- HIPAA: AI processing protected health information
- SOX / SEC guidance: AI in financial reporting and trading
- EEOC: AI in hiring and employment decisions
- FTC: AI in consumer-facing applications and advertising
State-Level AI Legislation
Rapidly evolving, 2024-2026 wave of legislationVaries by state; California, Colorado, Illinois leading
- California: AI transparency in government, deepfake disclosures
- Colorado: AI in insurance underwriting regulations
- Illinois: Biometric Information Privacy Act (BIPA) applies to AI
- Multiple states: AI in hiring bias legislation
Important Disclaimer
This is not legal advice. The information above is provided for awareness and planning purposes only. AI regulations vary by jurisdiction, industry, and use case. Consult qualified legal counsel for compliance requirements specific to your organization and jurisdiction. We help you build the operational framework -your legal team ensures it meets your specific obligations.
What a Good AI Policy Covers
This is the framework. We customize every section for your organization's industry, size, risk profile, and existing governance structures.
Purpose & Scope
Define why this policy exists, which AI systems it covers, and who it applies to. Include both in-house models and third-party AI tools (ChatGPT, Copilot, vendor APIs).
Definitions
Establish shared vocabulary: what counts as AI, machine learning, automated decision-making, and human-in-the-loop. Ambiguity here creates loopholes.
Roles & Responsibilities
Name the AI governance committee (or owner), define department-level responsibilities, and establish escalation paths. Every AI system needs an accountable human.
Acceptable Use
What AI can and cannot be used for. Explicitly list prohibited uses (e.g., automated termination decisions without human review) alongside encouraged uses.
Data Handling
Rules for training data sourcing, personal data in AI pipelines, data retention, and cross-border data transfers. Must align with your existing data governance policy.
Vendor & Third-Party Evaluation
Criteria for evaluating AI vendors: model transparency, data handling practices, SOC 2 compliance, incident history, and contractual protections.
Monitoring & Reporting
Performance monitoring cadence, bias audit schedule, reporting templates, and dashboard requirements. Include both automated and human review processes.
Incident Response
Define AI-specific incidents, severity levels, notification timelines, remediation steps, and post-incident review process.
Training Requirements
Who needs AI governance training, how often, and what it covers. Different roles need different depth: executives need risk literacy, developers need implementation practices.
Review Schedule
The policy itself must be a living document. Quarterly reviews of high-risk systems, annual full policy review, and trigger-based reviews when regulations change.
This is the starting point -not the finished product
Every organization has a unique AI footprint, risk tolerance, and regulatory environment. We take this framework, conduct a governance assessment of your current state, and build a policy that your teams will actually follow -not a shelf document that gathers dust.
Common Governance Mistakes
We have seen these patterns across dozens of organizations. Governance fails not because companies do not care, but because they fall into predictable traps.
Overly Restrictive Policies
Governance that blocks all AI experimentation kills innovation and drives shadow AI adoption. When the official process takes 6 months, teams use AI anyway -without guardrails.
The fix: Create a fast-track approval for low-risk AI use cases. Sandbox environments let teams experiment safely without full governance review.
Governance by Committee
A 12-person review board that meets monthly cannot keep pace with AI adoption. By the time they approve a use case, the technology has moved on and the business opportunity has passed.
The fix: Empower a small, empowered governance team (3-5 people) with clear decision authority. Reserve the full committee for high-risk approvals only.
No Enforcement Mechanism
A beautiful policy document that nobody follows is worse than no policy at all. It creates liability: you knew the risks and documented them, then failed to act.
The fix: Tie governance compliance to existing processes: code review gates, procurement checklists, and regular audits with teeth.
Treating Governance as a One-Time Project
AI governance is not a deliverable you complete and file away. The regulatory landscape, technology capabilities, and organizational AI use all change continuously.
The fix: Build governance as an ongoing program with a dedicated owner, regular review cadence, and a budget. It is infrastructure, not a project.
Frequently Asked Questions
Do we need AI governance if we only use third-party AI tools like ChatGPT or Copilot?
How long does it take to implement an AI governance framework?
Is this the same as AI ethics? What is the difference?
We are a small company. Is AI governance only for enterprises?
How do you stay current with the rapidly changing regulatory landscape?
What if we already have compliance and legal teams? Why do we need outside help?
Governance is the foundation. Start building yours.
The organizations that get AI governance right today will move faster -not slower- than their competitors. A clear framework removes friction, builds trust, and gives your teams the confidence to innovate responsibly.
We will assess your current AI footprint, identify governance gaps, and deliver a customized framework your teams can implement immediately.
The cost of governance is predictable. The cost of ungoverned AI is not.
Or email us directly: business@whataboutai.com
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